Quantitative Marketing and Economics

, Volume 14, Issue 4, pp 353–384 | Cite as

The palette that stands out: Color compositions of online curated visual UGC that attracts higher consumer interaction

  • Nima Y. Jalali
  • Purushottam PapatlaEmail author


Photos posted by consumers on social media, like Instagram, often include brands. Despite the substantial increase in such photos, there have been few investigations into how prospective consumers respond to this visual UGC. We begin to address this gap by investigating the role of the color compositions of visual UGC in consumer response. Consumer response is operationalized as the click-rate for a photo by a consumer when it is curated on the online site of the brand that it includes. This is the proportion of visitors who click on it for an enlarged view. Composition is operationalized as the specific combination of levels of the photo’s color attributes: hue, chroma, and brightness. Our goal is to identify the color compositions of photos, ceteris paribus, which get more clicks when they are curated. Data for our investigation comes from clicks over a one-year period on photos posted on Instagram curated by fifteen brands in six product categories on their sites. We assume Beta distributed proportions and calibrate a Beta regression using MCMC methods for our investigation.

We find that click-rates are higher for photos that include higher proportions of green and lower proportions of red and cyan. We also find that chroma of red and blue are higher in photos with higher click-rates. Findings from our research led the sponsoring firm to modify its proprietary curation algorithm for client brands. The firm informed us that, post-modification, there has been a substantial increase in click-rates of curated photos for brands in several categories.


Visual UGC Color composition Click-rates Bayesian Beta regression 



The authors are grateful to Pradeep Chintagunta and Sanjog Misra for a very constructive and helpful review process. They would also like to thank two anonymous reviewers for their insightful comments. They also express their gratitude to the agency’s founders for providing the data and being always readily available for discussions and clarifications during the research. They also thank Lakshman Krishnamurthi, Don Lehmann, Puneet Manchanda, Anita Rao and attendees of the Big Data Marketing Analytics Conference at the University of Chicago’s Gleacher Center on October 31, 2014, for comments during the early stages of this research.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Belk College of BusinessUniversity of North Carolina-CharlotteCharlotteUSA
  2. 2.Lubar School of BusinessUniversity of Wisconsin-MilwaukeeMilwaukeeUSA

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